{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 读取数据\n",
    "import pandas as pd\n",
    "\n",
    "data = pd.read_csv(\"./data.csv\");\n",
    "\n",
    "# condition = ((data[\"语文成绩\"] < 60) | (data[\"数学成绩\"] < 60)) & (data[\"PASS\"] == 1);\n",
    "# 修改错误 将 符合条件的改为0\n",
    "# data.loc[condition, ['PASS']] = 0\n",
    "\n",
    "# data.to_csv(\"./data.csv\", index=False, encoding=\"utf-8-sig\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '数学-语文')"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制图表\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib.font_manager\n",
    " \n",
    "# 指定中文字体\n",
    "# 指定支持中文的字体\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei'] # 推荐使用SimHei字体显示中文\n",
    "plt.rcParams['axes.unicode_minus'] = False # 解决保存图像是负号'-'显示为方块的问题fig = plt.figure()\n",
    "mask = data.loc[:,'PASS']==1\n",
    "\n",
    "passed = plt.scatter(data.loc[:,u\"语文成绩\"][mask], data.loc[:,u\"数学成绩\"][mask])\n",
    "failed = plt.scatter(data.loc[:,u\"语文成绩\"][~mask], data.loc[:,u\"数学成绩\"][~mask])\n",
    "plt.ylabel(u\"数学\")\n",
    "plt.xlabel(u\"语文\")\n",
    "plt.legend((passed,failed), ('passed', 'failed'))\n",
    "plt.title(\"数学-语文\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[87 89]\n",
      " [87 91]\n",
      " [78 91]\n",
      " [89 88]\n",
      " [88 90]\n",
      " [91 89]\n",
      " [85 88]\n",
      " [88 87]\n",
      " [92 93]\n",
      " [86 86]\n",
      " [83 92]\n",
      " [88 90]\n",
      " [79 90]\n",
      " [89 85]\n",
      " [81 86]\n",
      " [90 88]\n",
      " [90 91]\n",
      " [86 87]\n",
      " [90 89]\n",
      " [89 87]\n",
      " [85 90]\n",
      " [85 91]\n",
      " [90 87]\n",
      " [90 89]\n",
      " [80 89]\n",
      " [88 93]\n",
      " [83 92]\n",
      " [57 88]\n",
      " [88 85]\n",
      " [92 87]\n",
      " [87 58]\n",
      " [87 90]\n",
      " [91 88]\n",
      " [86 92]\n",
      " [59 89]\n",
      " [32 94]\n",
      " [58 90]\n",
      " [38 89]\n",
      " [91  0]\n",
      " [29 93]\n",
      " [33 42]\n",
      " [40 90]\n",
      " [52 88]\n",
      " [78 85]\n",
      " [86 87]\n",
      " [89 92]\n",
      " [82 91]\n",
      " [34 57]\n",
      " [50 41]\n",
      " [24 25]\n",
      " [50 24]\n",
      " [53 22]\n",
      " [26 33]\n",
      " [35 29]\n",
      " [87 86]\n",
      " [85 89]\n",
      " [88 91]\n",
      " [87 85]\n",
      " [86 88]\n",
      " [90 93]\n",
      " [92 87]\n",
      " [92 91]\n",
      " [85 87]\n",
      " [83 86]\n",
      " [87 90]\n",
      " [84 86]\n",
      " [89 50]\n",
      " [47 88]\n",
      " [48 89]\n",
      " [89 50]\n",
      " [85 58]\n",
      " [53 92]\n",
      " [55 93]\n",
      " [90  0]\n",
      " [42 89]\n",
      " [90 42]\n",
      " [86 19]\n",
      " [87 85]\n",
      " [51  0]\n",
      " [91 52]\n",
      " [95 51]\n",
      " [88 52]\n",
      " [93 53]\n",
      " [90 58]\n",
      " [87 59]\n",
      " [93 57]\n",
      " [91 57]\n",
      " [87 53]\n",
      " [55  0]\n",
      " [95 57]\n",
      " [95 57]\n",
      " [55  0]\n",
      " [85 57]\n",
      " [89 50]\n",
      " [53  0]\n",
      " [91 50]\n",
      " [91 59]\n",
      " [57  0]]\n"
     ]
    }
   ],
   "source": [
    "# \n",
    "X = data.iloc[:,[1,2]].to_numpy();\n",
    "chinese = data.loc[:,'语文成绩']\n",
    "math = data.loc[:,'数学成绩']\n",
    "y=data.loc[:,'PASS']\n",
    "print(X)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 训练数据\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "lr = LogisticRegression();\n",
    "# 训练数据\n",
    "lr.fit(X,y);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 0 0 0\n",
      " 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0\n",
      " 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "# 预测结果\n",
    "y_predict = lr.predict(X);\n",
    "print(y_predict);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型表现: 1.000000\n"
     ]
    }
   ],
   "source": [
    "# 评估模型表现\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "accuracy = accuracy_score(y,y_predict);\n",
    "\n",
    "print(\"模型表现: %f\" % accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "failed\n"
     ]
    }
   ],
   "source": [
    "# 预测成绩是否通过\n",
    "y_test = lr.predict([[70,60]])\n",
    "\n",
    "print(\"passed\" if y_test==1 else \"failed\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "截距：-87.340632, 系数1：0.517091, 系数2：0.587628\n",
      "0      72.075697\n",
      "1      72.075697\n",
      "2      79.995362\n",
      "3      70.315771\n",
      "4      71.195734\n",
      "         ...    \n",
      "93     70.315771\n",
      "94    101.994433\n",
      "95     68.555846\n",
      "96     68.555846\n",
      "97     98.474581\n",
      "Name: 语文成绩, Length: 98, dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "X:\\TMP\\ipykernel_15228\\4484535.py:6: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  print(\"截距：%f, 系数1：%f, 系数2：%f\" %(theta_0, theta_1, theta_2))\n"
     ]
    }
   ],
   "source": [
    "#  截距\n",
    "theta_0 = lr.intercept_;\n",
    "# 系数\n",
    "theta_1, theta_2 =lr.coef_[0][0], lr.coef_[0][1];\n",
    "# theta_1 x1 + theta_2 x2 + theta_0 = 0\n",
    "print(\"截距：%f, 系数1：%f, 系数2：%f\" %(theta_0, theta_1, theta_2))\n",
    "math_new = -(theta_0 + theta_1*chinese) / theta_2;\n",
    "print(math_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制方程\n",
    "figResult = plt.figure();\n",
    "passed = plt.scatter(data.loc[:,u\"语文成绩\"][mask], data.loc[:,u\"数学成绩\"][mask])\n",
    "failed = plt.scatter(data.loc[:,u\"语文成绩\"][~mask], data.loc[:,u\"数学成绩\"][~mask])\n",
    "plt.plot(math_new, chinese)\n",
    "plt.ylabel(u\"数学\")\n",
    "plt.xlabel(u\"语文\")\n",
    "plt.legend((passed,failed), ('passed', 'failed'))\n",
    "plt.title(\"数学-语文\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
